A systematic review of the geospatial barriers to antiretroviral initiation, adherence and viral suppression among people living with HIV
Bibliographic record
Abstract
Background With the emergence of antiretroviral therapy (ART), Treatment as Prevention (TasP) has become the cornerstone of both HIV clinical care and HIV prevention. However, despite the efficacy of treatment-based programs and policies, structural barriers to ART initiation, adherence and viral suppression have the potential to reduce TasP effectiveness. These barriers have been studied using Geographic Information Systems (GIS). While previous reviews have examined the use of GIS for HIV testing - an essential antecedent to clinical care - to date, no reviews have summarised the research with respect to other ART-related outcomes. METHODS: Therefore, the present review leveraged the PubMed database to identify studies that leveraged GIS to examine the barriers to ART initiation, adherence and viral suppression, with the overall goal of understanding how GIS has been used (and might continue to be used) to better study TasP outcomes. Joanna Briggs Institute criteria were used for the critical appraisal of included studies. RESULTS: In total, 33 relevant studies were identified, excluding those not utilising explicit GIS methodology or not examining TasP-related outcomes. CONCLUSIONS: Findings highlight geospatial variation in ART success and inequitable distribution of HIV care in racially segregated, economically disadvantaged, and, by some accounts, increasingly rural areas - particularly in the United States. Furthermore, this review highlights the utility and current limitations of using GIS to monitor health outcomes related to ART and the need for careful planning of resources with respect to the geospatial movement and location of people living with HIV (PLWH).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".